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Hierarchical Gaussian Mixture Normalizing Flows Modeling for Unified Anomaly Detection

PyTorch implementation for ECCV2024 paper, Hierarchical Gaussian Mixture Normalizing Flows Modeling for Unified Anomaly Detection.

Installation

Install all packages with this command:

$ python3 -m pip install -U -r requirements.txt

Download Datasets

Please download MVTecAD dataset from MVTecAD dataset, BTAD dataset from BTAD dataset, MVTecAD-3D dataset from MVTecAD-3D dataset, and VisA dataset VisA dataset.

Training

• Run code for training MVTecAD

python main.py --dataset mvtec --seed 0 --gpu 0

Normally, you can obtain the following results:

• Run code for training BTAD

python main.py --dataset btad --seed 0 --gpu 0

Normally, you can obtain the following results:

• Run code for training MVTecAD-3D

python main.py --dataset mvtec3d --seed 0 --gpu 0

Normally, you can obtain the following results:

• Run code for training VisA

python main.py --dataset visa --seed 0 --gpu 0

Normally, you can obtain the following results:

• Run code for training Union dataset (combined by MVTecAD, BTAD, MVTecAD-3D, and VisA)

python main.py --dataset union --seed 0 --gpu 0

We also report the detailed results on the Union dataset as follows:

Note: You need to set the root directory of your dataset in the main.py by setting args.data_path. For Union dataset, the dataset path can be set in the datasets/union.py script.

Citation

If you find this repository useful, please consider citing our work:

@article{HGAD,
      title={Hierarchical Gaussian Mixture Normalizing Flows Modeling for Unified Anomaly Detection}, 
      author={Xincheng Yao and Ruoqi Li and Zefeng Qian and Lu Wang and Chongyang Zhang},
      year={2024},
      booktitle={European Conference on Computer Vision 2024},
      url={https://arxiv.org/abs/2403.13349},
      primaryClass={cs.CV}
}

If you are interested in our work, you can also follow our other works: BGAD (CVPR2023), PMAD (AAAI2023), FOD (ICCV2023), ResAD (NeurIPS2024). Or, you can follow our github page xcyao00.